Treatment Effect In Meta-analysis Research Articles

ZIBGLMM: Zero-Inflated Bivariate Generalized Linear Mixed Model for Meta-Analysis with Double-Zero-Event Studies.

Double-zero-event studies (DZS) pose a challenge for accurately estimating the overall treatment effect in meta-analysis. Current approaches, such as continuity correction or omission of DZS, are commonly employed, yet these ad hoc methods can yield biased conclusions. Although the standard bivariate generalized linear mixed model can accommodate DZS, it fails to address the potential systemic differences between DZS and other studies. In this paper, we propose a zero-inflated bivariate generalized linear mixed model (ZIBGLMM) to tackle this issue. This two-component finite mixture model includes zero-inflation for a subpopulation with negligible or extremely low risk. We develop both frequentist and Bayesian versions of ZIBGLMM and examine its performance in estimating risk ratios (RRs) against the bivariate generalized linear mixed model and conventional two-stage meta-analysis that excludes DZS. Through extensive simulation studies and real-world meta-analysis case studies, we demonstrate that ZIBGLMM outperforms the bivariate generalized linear mixed model and conventional two-stage meta-analysis that excludes DZS in estimating the true effect size with substantially less bias and comparable coverage probability.

Confidence intervals for random effects meta-analysis and robustness to publication bias

The DerSimonian-Laird confidence interval for the average treatment effect in meta-analysis is widely used in practice when there is heterogeneity between studies. However, it is well known that its coverage probability (the probability that the interval actually includes the true value) can be substantially below the target level of 95 per cent. It can also be very sensitive to publication bias. In this paper, we propose a new confidence interval that has better coverage than the DerSimonian-Laird method, and that is less sensitive to publication bias. The key idea is to note that fixed effects estimates are less sensitive to such biases than random effects estimates, since they put relatively more weight on the larger studies and relatively less weight on the smaller studies. Whereas the DerSimonian-Laird interval is centred on a random effects estimate, we centre our confidence interval on a fixed effects estimate, but allow for heterogeneity by including an assessment of the extra uncertainty induced by the random effects setting. Properties of the resulting confidence interval are studied by simulation and compared with other random effects confidence intervals that have been proposed in the literature. An example is briefly discussed.

Mixed treatment comparison analysis provides internally coherent treatment effect estimates based on overviews of reviews and can reveal inconsistency

ObjectivesTo propose methods for mixed treatment comparisons (MTC) based on pooled summaries of the type produced in overviews of reviews. Study Design and SettingOverviews of reviews (umbrella reviews) summarize the results of multiple systematic reviews into a single document. They report the summary estimates from the original pairwise meta-analyses and discuss them in narrative form, with the intention of identifying the most effective treatment. We present methods for MTC synthesis, tailored for use with overviews of reviews. These generate a single internally consistent summary of all the relative treatment effects and assessments of whether the summary is consistent with the data. These methods are applied to a published overview of treatments for childhood nocturnal enuresis. We apply the methods to both fixed-effect (FE) and random-effects (RE) meta-analyses of the original trials. ResultsThe summary relative risks based on FE meta-analyses, as originally published, were highly inconsistent. Those based on RE meta-analyses were consistent and could, given standard assumptions on comparability of treatment effects in meta-analysis, form a basis for coherent decision making. ConclusionAlong with the summaries from systematic reviews, MTC methods should be used in overviews to provide a single coherent analysis of all treatment comparisons and to check for evidence consistency.

The Significance Level of the Standard Test for a Treatment Effect in Meta-analysis

The random effects model is routinely used in meta-analysis. Although a variety of approaches have been proposed for implementing this in practice, the most popular method is that suggested by DerSimonian and Laird. The resulting inferences are not exact, however, and modifications to the methodology have been suggested. By deriving the exact distribution of the resulting test statistic, under the null hypothesis and in the simplified scenario where all studies are the same size, it is possible to obtain the actual significance level of the standard hypothesis test for a treatment effect. In particular it is found that, if there is a small number of studies and considerable between-study variation, the actual significance level can be much larger than the nominal level. This and other findings are illustrated using examples concerning the effectiveness of treatments to reduce serum cholesterol for preventing death in patients with no history of heart attacks, the use of glycerol for patients who have suffered an acute stroke, and the use of amisulpride to treat schizophrenia.

A robust P‐value for treatment effect in meta‐analysis with publication bias

Publication bias is a major and intractable problem in meta-analysis. There have been several attempts in the literature to adapt methods to allow for such bias, but these are only possible if we are prepared to make strong assumptions about the underlying selection mechanism. We discuss the assumption that the probability that a paper is published may depend in some unspecified way on the P-value being claimed by that study. We suggest a new robust P-value for the overall treatment effect, which turns out to be closely related to the correlation of the associated radial plot. Properties of the method are discussed and illustrated in two examples.

Generalized Inferences on the Overall Treatment Effect in Meta‐analysis with Normally Distributed Outcomes

This paper focuses on inferences about the overall treatment effect in meta-analysis with normally distributed responses based on the concepts of generalized inference. A refined generalized pivotal quantity based on t distribution is presented and simulation study shows that it can provide confidence intervals with satisfactory coverage probabilities and perform hypothesis testing with satisfactory type-I error control at very small sample sizes.

A comparison of three different models for estimating relative risk in meta‐analysis of clinical trials under unobserved heterogeneity

We focus on the comparison of three statistical models used to estimate the treatment effect in meta-analysis when individually pooled data are available. The models are two conventional models, namely a multi-level and a model based upon an approximate likelihood, and a newly developed model, the profile likelihood model which might be viewed as an extension of the Mantel-Haenszel approach. To exemplify these methods, we use results from a meta-analysis of 22 trials to prevent respiratory tract infections. We show that by using the multi-level approach, in the case of baseline heterogeneity, the number of clusters or components is considerably over-estimated. The approximate and profile likelihood method showed nearly the same pattern for the treatment effect distribution. To provide more evidence two simulation studies are accomplished. The profile likelihood can be considered as a clear alternative to the approximate likelihood model. In the case of strong baseline heterogeneity, the profile likelihood method shows superior behaviour when compared with the multi-level model.